These and many other fascinating insights are from the Cowen and Company Multi-Sector Equity Research study, Artificial Intelligence: Entering A Golden Age For Data Science (142 pp., PDF, client access reqd).

The study is based on interviews with 146 leading AI researchers, entrepreneurs and VC executives globally who are involved in the field of artificial intelligence and related technologies. Please see the Appendix of the study for a thorough overview of the methodology. This study isn’t representative of global AI, data engineering and machine learning (ML) adoption trends. It does, however, provide a glimpse into the current and future direction of AI, data engineering, and machine learning.

Cowen finds the market is still nascent, with CIOs eager to invest in new AI-related initiatives. Time-to-market, customer messaging, product positioning and the value proposition of AI solutions will be critical factors for winning over new project investments.

Key takeaways from the study include the following:

Digital marketing/marketing automation, Salesforce automation (CRM) and data analytics are the top three areas ripe for AI/ML adoption

Customer self-service, enterprise resource planning (ERP), human resource management (HRM) and eCommerce are additional areas that have upside potential for AI/ML adoption. The following graphic provides an overview of the areas in software that Cowen found the greater potential for AI/ML investment.

81% of IT leaders are currently investing in or planning to invest in artificial intelligence (AI)

Based on the study, CIOs have a new mandate to integrate AI into IT technology stacks. The study found that 43% are evaluating and doing a Proof of Concept (POC) and 38% are already live and planning to invest more. The following graphic provides an overview of company readiness for machine learning and AI projects.

Market forecasts vary, but all consistently predict explosive growth

IDC predicts that the Cognitive Systems and AI market (including hardware & services) will grow from $8B in 2016 to $47B in 2020, attaining a Compound Annual Growth Rate (CAGR) of 55%. This forecast includes $18B in software applications, $5B in software platforms, and $24B in services and hardware. IBM claims that cognitive computing is a $2T market, including $200B in healthcare/life sciences alone. Tractica forecasts direct and indirect applications of AI software to grow from $1.4B in 2016 to $59.8B by 2025, a 52% CAGR.

According to CBInsights, the number of financing transactions to AI startups increased 10x over the last six years, from 67 in 2011 to 698 in 2016

Accenture states that the total number of AI start-ups has increased 20-fold since 2011. The top verticals include fintech, healthcare, transportation and retail/eCommerce. The following graphic provides an overview of the AI annual funding history from 2011 to 2016.

Algorithmic trading, image recognition/tagging, and patient data processing are predicted to be the top AI uses cases by 2025

Tractica forecasts predictive maintenance and content distribution on social media will be the fourth and fifth highest revenue producing AI uses cases over the next eight years. The following graphic compares the top 10 uses cases by projected global revenue.

Machine learning is predicted to generate the most revenue and is attracting the most venture capital investment in all areas of AI

Venture Scanner found that ML raised $3.5B to date (from 400+ companies), far ahead of the next category, natural language processing, which has seen just over $1Bn raised to date (from 200+ companies). Venture Scanner believes that machine learning applications and machine learning platforms are two relatively early stage markets that stand to have some of the greatest market disruptions.

Cowen predicts that an Intelligent App Stack will gain rapid adoption in enterprises as IT departments shift from system-of-record to system-of-intelligence apps, platforms, and priorities

The future of enterprise software is being defined by increasingly intelligent applications today, and this will accelerate in the future. Cowen predicts it will be commonplace for enterprise apps to have machine learning algorithms that can provide predictive insights across a broad base of scenarios encompassing a company’s entire value chain. The potential exists for enterprise apps to change selling and buying behavior, tailoring specific responses based on real-time data to optimize discounting, pricing, proposal and quoting decisions.

Other forecasts

According to angel.co, there are 2,200+ artificial intelligence startups, and well over 50% have emerged in just the last two years. Machine learning-based applications and deep learning neural networks are experiencing the largest and widest amount of investment attention in the enterprise.

Accenture leverages machine learning in 40% of active Analytics engagements, and nearly 80% of proposed analytics opportunities today. Cowen found that Accenture’s view is that they are in the early stages of AI technology adoption with their enterprise clients. Accenture sees the AI market growing exponentially, reaching $400B in spending by 2020. Their customers have moved on from piloting and testing AI to reinventing their business strategies and models.

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